In [27]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math
import re
In [28]:
df = pd.read_csv("Bengaluru_House_Data.csv")
df.head()
Out[28]:
area_type availability location size society total_sqft bath balcony price
0 Super built-up Area 19-Dec Electronic City Phase II 2 BHK Coomee 1056 2.0 1.0 39.07
1 Plot Area Ready To Move Chikka Tirupathi 4 Bedroom Theanmp 2600 5.0 3.0 120.00
2 Built-up Area Ready To Move Uttarahalli 3 BHK NaN 1440 2.0 3.0 62.00
3 Super built-up Area Ready To Move Lingadheeranahalli 3 BHK Soiewre 1521 3.0 1.0 95.00
4 Super built-up Area Ready To Move Kothanur 2 BHK NaN 1200 2.0 1.0 51.00
In [29]:
#finding correlation values within the dataset
#we remove features which are highly related to each other as they do not provide
#any significance value to our Model
corr = df.corr()
plt.figure(figsize = (10,8))
sns.heatmap(corr ,annot=True,cmap='Blues')
Out[29]:
<AxesSubplot:>
2020-10-13T20:27:19.685572 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
In [30]:
data = df.drop(columns=['area_type', 'availability','society','bath','balcony'])
data.head()
Out[30]:
location size total_sqft price
0 Electronic City Phase II 2 BHK 1056 39.07
1 Chikka Tirupathi 4 Bedroom 2600 120.00
2 Uttarahalli 3 BHK 1440 62.00
3 Lingadheeranahalli 3 BHK 1521 95.00
4 Kothanur 2 BHK 1200 51.00
In [31]:
data.shape
Out[31]:
(13320, 4)
In [32]:
print(data.isnull().sum())
sns.heatmap(data.isnull())

location       1
size          16
total_sqft     0
price          0
dtype: int64
Out[32]:
<AxesSubplot:>
2020-10-13T20:27:21.301662 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
In [33]:
# Handling Missing values
data['location'] = data['location'].fillna('Sarjapur  Road')
data['size'] = data['size'].fillna('3 BHK')
In [34]:
data.head()
Out[34]:
location size total_sqft price
0 Electronic City Phase II 2 BHK 1056 39.07
1 Chikka Tirupathi 4 Bedroom 2600 120.00
2 Uttarahalli 3 BHK 1440 62.00
3 Lingadheeranahalli 3 BHK 1521 95.00
4 Kothanur 2 BHK 1200 51.00
In [35]:
# Removing outliers in 'SIZE'
data['size']=data['size'].replace('1 Bedroom','1')
data['size']=data['size'].replace('2 Bedroom','2')
data['size']=data['size'].replace('3 Bedroom','3')
data['size']=data['size'].replace('4 Bedroom','4')
data['size']=data['size'].replace('5 Bedroom','5')
data['size']=data['size'].replace('6 Bedroom','6')
data['size']=data['size'].replace('1 BHK','1')
data['size']=data['size'].replace('2 BHK','2')
data['size']=data['size'].replace('3 BHK','3')
data['size']=data['size'].replace('4 BHK','4')
data['size']=data['size'].replace('5 BHK','5')
data['size']=data['size'].replace('6 BHK','6')
data['size']=data['size'].replace([s for s in data['size'] if s not in ['1','2','3','4','5','6']],'3')
data['size'].value_counts()
Out[35]:
2    5528
3    5155
4    1417
1     643
5     356
6     221
Name: size, dtype: int64
In [37]:
# Renaming the columns to correct names
data = data.rename(columns={'size':'BHK','total_sqft':'sqft'})
data.head()
Out[37]:
location BHK sqft price
0 Electronic City Phase II 2 1056 39.07
1 Chikka Tirupathi 4 2600 120.00
2 Uttarahalli 3 1440 62.00
3 Lingadheeranahalli 3 1521 95.00
4 Kothanur 2 1200 51.00
In [38]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13320 entries, 0 to 13319
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   location  13320 non-null  object 
 1   BHK       13320 non-null  object 
 2   sqft      13320 non-null  object 
 3   price     13320 non-null  float64
dtypes: float64(1), object(3)
memory usage: 416.4+ KB
In [39]:
# Changing the incorrect data type 
data['BHK']=data['BHK'].astype('category')
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13320 entries, 0 to 13319
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   location  13320 non-null  object  
 1   BHK       13320 non-null  category
 2   sqft      13320 non-null  object  
 3   price     13320 non-null  float64 
dtypes: category(1), float64(1), object(2)
memory usage: 325.5+ KB
In [40]:
def convert_sqft_to_num(x):
    tokens = x.split('-')
    if len(tokens) == 2:
        return (float(tokens[0])+float(tokens[1]))/2
    try:
        return float(x)
    except:
        return None
## applying the fucntion to the column: - 'total_sqft'
data.sqft = data.sqft.apply(convert_sqft_to_num)
# Taking only the Numeric values from the data and storing it in 'home'
data = data[data.sqft.notnull()]
# display the first 2 columns from the dataset
data.head(2)
Out[40]:
location BHK sqft price
0 Electronic City Phase II 2 1056.0 39.07
1 Chikka Tirupathi 4 2600.0 120.00
In [41]:
#checking the dataset with highest location data provided
#because havind values for a location less than 10 wont give us good information on the dataset
data.location = data.location.str.strip()
location_stats = data['location'].value_counts(ascending=False)
location_stats
Out[41]:
Whitefield                         539
Sarjapur  Road                     400
Electronic City                    304
Kanakpura Road                     271
Thanisandra                        236
                                  ... 
AECS LAYOUT A-BLOCK Singasandra      1
KG Halli                             1
Lakshmipura Vidyaanyapura            1
Sree Narayana Nagar                  1
Sector 4 HSR Layout                  1
Name: location, Length: 1288, dtype: int64
In [42]:
#cretaing a Series of all the location having less than 10 entries against its  
location_stats_less_than_10 = location_stats[location_stats<=10]
location_stats_less_than_10
Out[42]:
Sadashiva Nagar                    10
Ganga Nagar                        10
1st Block Koramangala              10
Kalkere                            10
Dodsworth Layout                   10
                                   ..
AECS LAYOUT A-BLOCK Singasandra     1
KG Halli                            1
Lakshmipura Vidyaanyapura           1
Sree Narayana Nagar                 1
Sector 4 HSR Layout                 1
Name: location, Length: 1048, dtype: int64
In [43]:
#using lambda function to naming 'location_stats_less_than_10' as 'other' and then removing it
data.location = data.location.apply(lambda x: 'other' if x in location_stats_less_than_10 else x)
data = data[data.location != 'other']
data.shape
Out[43]:
(10398, 4)
In [49]:
data['price_per_sqft'] = data['price']*100000/data['sqft']
data
Out[49]:
location BHK sqft price price_per_sqft
0 1st Block Jayanagar 4 2850.0 428.00 15017.543860
1 1st Block Jayanagar 3 1630.0 194.00 11901.840491
2 1st Block Jayanagar 6 1200.0 125.00 10416.666667
3 1st Block Jayanagar 3 1875.0 235.00 12533.333333
4 1st Block Jayanagar 3 930.0 85.00 9139.784946
... ... ... ... ... ...
8620 Yeshwanthpur 3 1676.0 92.13 5497.016706
8621 Yeshwanthpur 3 2503.0 138.00 5513.383939
8622 Yeshwanthpur 3 1855.0 135.00 7277.628032
8623 Yeshwanthpur 3 1876.0 160.00 8528.784648
8624 Yeshwanthpur 3 1675.0 92.13 5500.298507

8625 rows × 5 columns

In [50]:
data['price_per_sqft'].describe()
Out[50]:
count     8625.000000
mean      5741.547053
std       2480.502632
min       1150.172117
25%       4250.000000
50%       5210.526316
75%       6500.000000
max      26973.684211
Name: price_per_sqft, dtype: float64
In [46]:
## as per Normal Distribution, 95% of our data lies within 1st Standard Deviation as per the location
def remove_pps_outliers(df):
    df_out = pd.DataFrame()
    for key, subdf in df.groupby('location'):
        m = np.mean(subdf.price_per_sqft)
        st = np.std(subdf.price_per_sqft)
        reduced_df = subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]
        df_out = pd.concat([df_out,reduced_df],ignore_index=True)
    return df_out
data = remove_pps_outliers(data)
data.shape
Out[46]:
(8625, 5)
In [47]:
## representing Numerical Data and Visualizing the same usin Distplot to gain further info
num_ = data.select_dtypes(exclude = 'object')
fig = plt.figure(figsize =(10,8))
for index, col in enumerate(num_):
    plt.subplot(3,2,index+1)
    sns.distplot(num_.loc[:,col],kde = False)
fig.tight_layout(pad = 1.0)
2020-10-13T20:27:51.497422 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
In [48]:
# To save dataframe to csv
# data.sort_values(by='location')
data = data.drop(columns=['price_per_sqft'])
data.reset_index(drop = True)
data.to_csv('Cleanned_data.csv', index=False)
data
Out[48]:
location BHK sqft price
0 1st Block Jayanagar 4 2850.0 428.00
1 1st Block Jayanagar 3 1630.0 194.00
2 1st Block Jayanagar 6 1200.0 125.00
3 1st Block Jayanagar 3 1875.0 235.00
4 1st Block Jayanagar 3 930.0 85.00
... ... ... ... ...
8620 Yeshwanthpur 3 1676.0 92.13
8621 Yeshwanthpur 3 2503.0 138.00
8622 Yeshwanthpur 3 1855.0 135.00
8623 Yeshwanthpur 3 1876.0 160.00
8624 Yeshwanthpur 3 1675.0 92.13

8625 rows × 4 columns

In [ ]: